During Coursera machine learning course by Andrew NG in the week 7 assignment(Ex 6), many may find this same problem to visualize data in octave.Problem while executing ex6.m file
But I cannot find any solution because I should not touch the visualization code as previously instructed.
Thus I cannot find any solution and not getting the full marks at coding
The problem with this is the Octave version. Previously like this:
contour(X1, X2, vals, [0 0], 'Color', 'b');
But this is invalid in new versions. So instead you have to use one of these way Conture function parameters(official)
So to solve the problem, you have to go to your visualBoundary.m file and change the counture function with
contour(X1,X2,vals,'--');
This will solve the visualization problem
Related
I'm learning some data science related topics and oh boy, this is a jungle of different libraries for everything 😅
Because of things, I went with Lets-plot, which has a nice Kotlin API that I'm using combined with Kotlin kernel for Jupyter notebooks
Overall, things are going pretty good. Most tutorials & docs I see online use different libraries for plotting (e.g. Seaborn, Matplotlib, Plotly) so most of the time I have to do some reading of the Lets-Plot-Kotlin reference and try/error until I find the equivalent code for my graphs
Currently, I'm trying to graph the distribution of differences between two values. Overall, this looks pretty good. I can just do something like
(letsPlot(df)
+ geomHistogram { x = "some-column" }
).show()
which gives a nice graph
It would be interesting to see the density estimator as well, geomDensity to the rescue!
(letsPlot(df)
+ geomDensity(color = "red") { x = "some-column" }
).show()
Nice! Now let's watch them both together
(letsPlot(df)
+ geomDensity(color = "red") { x = "some-column" }
+ geomHistogram() { x = "some-column" }
).show()
As you can see, there's a small red line in the bottom (the geomDensity!). Problem here (I would say) is that both layers are using the same Y scale. Histogram is working with 0-20 values and density with 0-0.02 so when plotted together it's just a line at the bottom
Is there any way to add several layers in the same plot that use their own scale? I've read some blogposts that claim that you should not go for it (seems to be pretty much accepted by the community.
My target is to achieve something similar to what you can do with Seaborn by doing
plt.figure(figsize=(10,4),dpi=200)
sns.histplot(data=df,x='some_column',kde=True,bins=25)
(yes I know I took the lets plot screenshot without the bins configured. Not relevant, I'd say ¯_(ツ)_/¯ )
Maybe I'm just approaching the problem with a mindset I should not? As mentioned, I'm still learning so every alternative will be highly welcomed 😃
Just, please, don't go with the "Switch to Python". I'm exploring and I'd prefer to go one topic at a time
In order for histogram and density layers to share the same y-scale you need to map variable "..density.." to aesthetic "y" in the histogram layer (by default histogram maps "..count.." to "y").
You will find an example of it in cell [4] in this notebook: https://nbviewer.org/github/JetBrains/lets-plot-kotlin/blob/master/docs/examples/jupyter-notebooks/distributions.ipynb
BWT, many of the pages in Lets-Plot Kotlin API Reference are equipped with links on demo-notebooks, in "Examples" section: geomHistogram().
And of course you can find a lot of info online on the R ggplot2 package which is largely applicable to Lets-Plot as well. For example: Histogram with kernel density estimation.
Finally :) , calling show() is not necessary - Jupyter Kotlin kernel will render plot automatically if plot expression is the last one in the cell which is often the case.
I use the cleverhans code for cw to produce adversarial examples on Imagenet. The target model is InceptionV3(from keras) and I want to use cw for targeted attack. But when I save the adv image, they have changed a lot from the original images. I think maybe I use the wrong parameters. cw_params = {'binary_search_steps': 10,
'y_target': None,#(I specific the y_target later)
'max_iterations': 20000,
'learning_rate': .0002,
'batch_size': 1,
'initial_const': 10}
I have tried a lot of parameters, but I still can't find the great effects as carlini's paper. And when I use this parameter, the runing time is really long. I don't know the proper time.
#just some key codes:
temp_seeds=np.array(image.load_img(item_in_seed,target_size=(299,299)))
temp_seeds=np.expand_dims(temp_seeds,axis=0)
cw = CarliniWagnerL2(wrap, sess=sess)
cw_params = {'binary_search_steps': 10,
'y_target': None,#(I specific the y_target later)
'max_iterations': 20000,
'learning_rate': .0002,
'batch_size': 1,
'initial_const': 10}
adv= cw.generate_np(temp_seeds, **cw_params)
The successful examples of targeted attack have changed a lot from the original images in Imagenet. How can I get the small perturbation and the same great effects as the cw's paper
It is difficult to pin down the specific problem you are facing from this description but here are two suggestions:
Make sure the input domain is easy to optimize over (the CW paper has a change of variables to ensure that box constraints are respected).
Make sure that you are passing the right values from the model to the attack when it comes to computing the adversary's loss. It is often the case that numerical instabilities will prevent attacks from properly functioning.
Hope this helps!
I'm using tensorboard (tensorflow 1.1.0) to show the result of my CNN classifier.
I added some output vector as tf.summary.histogram in order to show the counts of output in each bin, but tensorboard seems to automatically compute interpolation and show them as (somehow) smoothed distribution
(and therefore I can not find the exact counts for the bins).
Could someone tell me how can I avoid the interpolation and show usual histograms using bars?
I not sure that there is easy way to do it.
I very unsure in below text, correct me if I wrong.
From this file https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/vz_histogram_timeseries/index.html it seems that histogram comes to tensorboard in double values.
Summary op uses either histogram from https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/python/ops/histogram_ops.py (1) or https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/core/lib/histogram/histogram.cc (2)
I suppose that it uses 2nd because here https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/python/summary/summary.py#L189 it calls function from generated file. In my package code in this generated file there is another function call:
result = _op_def_lib.apply_op("HistogramSummary", tag=tag, values=values,
name=name)
I have grep all repo and seems like there is no other python code which define something with "HistogramSummary", so it seems like it's really defined here https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/core/kernels/summary_op.cc and this code uses code mentioned above (2).
So, it seems to me that histogram which is used now is buried deep inside of framework and I not sure that it's easy to rewrite it.
In this page there is email for support https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/summary . I suppose that it's better to contact this person or make issue on github.
I am reading tutorials about TensorFlow visualization and found out Tensorboard. I would like to know how can I visualize for example, Iris dataset taken from UCI Machine Learning repository. I have been able to run a specified port on localhost which shows TensorBoard, but do not know how to visualize a locally taken dataset there. I searched on google but really could not find how to do. Could you help me, please ?
If i understand you correctly then you wish to use tf.summary.image. The documentation is here: https://www.tensorflow.org/api_docs/python/tf/summary/image
Some example usage from my code is:
x_pl=tf.placeholder(tf.float32, [None,height,width,channels], name="ImageIn")
tf.summary.image('input', x_pl, 10)
x_pl is where I feed my image data in.
In my cummary declaration I say that I want to create a summary called 'input' and to take 10 images from x_pl.
Read the summary-writer example/tutorial here: https://www.tensorflow.org/get_started/summaries_and_tensorboard
You will need to merge your summaries:
merged = tf.summary.merge_all()
You will need to declare a summary-writer a bit like this:
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',sess.graph)
See the above tutorial/example to understand how Tensorboard works. Not that you will want to replace the summaries with image summaries for your purposes.
TensorFlow provides a tf.summary.tensor_summary() function that appears to be a multidimensional variant of tf.summary.scalar():
tf.summary.tensor_summary(name, tensor, summary_description=None, collections=None)
I thought it could be useful for summarizing inferred probabilities per class ... somewhat like
op_summary = tf.summary.tensor_summary('classes', some_tensor)
# ...
summary = sess.run(op_summary)
writer.add_summary(summary)
However it appears that TensorBoard doesn't provide a way to display these summaries at all. How are they meant to be used?
I cannot get it to work either. It seems like that feature is still under development. See this video from the TensorFlow Dev Summit that states that the tensor_summary is still under development (starting at 9:17): https://youtu.be/eBbEDRsCmv4?t=9m17s. It will probably be better defined and examples should be provided in the future.